Computational Thinking

Computational Thinking

Computational Thinking

A problem-solving process involving techniques like Decomposition, Pattern Recognition, Abstraction, and Algorithms to solve complex problems efficiently.

Decomposition

Breaking down complex tasks into simpler steps to focus on solving one part at a time.

Pattern Recognition

Identifying patterns in data to predict outcomes or solve new problems effectively.

Abstraction

Focusing on essential details while ignoring irrelevant information to simplify problem-solving.

Algorithms

Step-by-step procedures for solving problems, fundamental in computational thinking and programming.

Top-Down Approach

Problem-solving method starting from the highest level and breaking it down into sub-problems.

Bottom-Up Approach

Problem-solving method starting from solving simple sub-problems and integrating them to solve the overall problem.

Divide and Conquer

Problem-solving technique involving dividing a problem into smaller sub-problems, solving each, and combining solutions.

Trial and Error

Problem-solving method of trying multiple solutions and learning from failures to find the correct solution.

Multiple-Choice Questions

Assessment method with one correct answer among several options to test understanding.

Short-Answer Questions

Assessment method requiring brief, specific answers to test knowledge and comprehension.

Long-Answer Questions

Assessment method requiring detailed explanations, often involving problem-solving or application of concepts.

Flowchart

A diagram that visually represents the flow of steps in a process or system.

Data Preprocessing

The process of cleaning and organizing data for analysis.

Python

A programming language known for its simplicity and readability, often used in teaching computational thinking.

Simulation

The practice of using computers to model real-world scenarios and solve problems.

Debugging

The systematic process of identifying and fixing bugs or errors in a program.

Automation

The method for automating repetitive tasks using scripts or software tools.

Data Analysis

The use of computational techniques to analyze and visualize data.

Workarounds

Temporary solutions that may not be perfect but are used to keep a system running.